Baggin_SVM

Code for handling imbalanced data（SVM/Bagging SVM/Deep Learning）
github [https://github.com/concrettojava/Bagging_SVM]

Requirements

• Python 3

• PyTorch 1.3+ (feel free if it's a CUDA or CPU version) (Test with 2.0)

• sklearn

• pandas

• matplotlib

Usage

• First of all, you need to change your dataset path
• This experiment was written based on JupyterNotebook.
• Of course, if you are having trouble using jupyter, you can run three deep learning models in py format files.

Parameters in DeepLearning models

• pos_weight
  Which determines the magnification of the gradient when calculating the loss of subcategory samples
  criterion = nn.BCEWithLogitsLoss(pos_weight=torch.tensor(5))
• threshold
  Classification confidence, as the final output of the model is a probability value, a normal 0.5 will directly lead to overfitting, which has a significant impact on the recall rate of subcategories
  threshold = 0.2

Issue

• Why did the output of the model not use sigmoid mapping to 0-1? Because the gradient calculation of sigmoid on small class samples was not significant during the experimental process, and the model was more overfitting
• If the deep learning model does not display an image after running it in jupyter notebook, you may need to add %matplotlib inline in the first line

Modified section

Added Minmax normalization in visualization